1. health and fitness
2. disease
3. aids and hiv

Vol. 6: 81–89, 2014
doi: 10.3354/aei00118
AQUACULTURE ENVIRONMENT INTERACTIONS
Aquacult Environ Interact
Published online December 2
OPEN
ACCESS
High host densities dilute sea lice Lepeophtheirus
salmonis loads on individual Atlantic salmon, but
do not reduce lice infection success
Francisca Samsing1,*, Frode Oppedal2, David Johansson2, Samantha Bui1,
Tim Dempster1, 3
1
Sustainable Aquaculture Laboratory − Temperate and Tropical (SALTT), Department of Zoology, University of Melbourne,
3010 Victoria, Australia
2
Institute of Marine Research, 5984 Matredal, Norway
3
Centre for Research-based Innovation in Aquaculture Technology (CREATE), SINTEF Fisheries and Aquaculture,
7465 Trondheim, Norway
ABSTRACT: Host density likely plays a key role in host−parasite interactions, but empirical evidence in marine ecosystems remains limited. Classical models predict a positive relationship
between host density and parasite infection parameters, but this depends on the parasite transmission mode. Evidence from systems where mobile parasites actively seek hosts suggests that numbers of parasites per host decrease with increasing host density (‘dilution effect’). Copepodids, the
infective stage of the salmon louse Lepeophtheirus salmonis, are mobile larvae that display a
range of behaviours to detect their salmonid hosts. We hypothesized that high host density would
decrease infection intensity, prevalence and degree of aggregation, but not infection success,
which reflects parasite performance. We infected multiple groups of Atlantic salmon Salmo salar
post-smolts at low (12 fish; 7.9 kg m−3) and high (96 fish; 68.5 kg m−3) densities, with the same
number of L. salmonis copepodids in swimming chambers to enable more realistic swimming
behaviours during infection. Infection intensity was 8.4 times higher in the low density treatment,
but there were no differences in infection success and degree of aggregation. We observed 100%
prevalence in the low density treatment, which was significantly higher than the high density
treatment (68%). The dilution effect most likely explained the negative relationship between host
density and infection intensity, as the individual risk of being ‘attacked’ by a parasite decreased
as host density increased. Host density is crucial in salmon−sea lice infection dynamics, and
opportunities may exist within production environments to use the dilution effect of density to
improve fish welfare outcomes.
KEY WORDS: Stocking density · Swimming speed · Parasite · Salmo salar
INTRODUCTION
Host density is a fundamental cornerstone of epidemiological theory and disease dynamics (Anderson & May 1991), but compelling experimental evidence is still scarce (Côté & Poulin 1995, Lloyd-Smith
et al. 2005). Classical models (Anderson & May 1978,
1979) predict that the mean number of parasites per
*Corresponding author: [email protected]
host (i.e. infection intensity) increases with higher
host densities, due to the higher probability of encountering a host (Arneberg et al. 1998). However,
examples of negative correlations between infection
intensity and host density exist (McCallum et al.
2001). Exceptions are found in host−parasite systems
where parasites encounter a limited number of hosts
that they can attack per unit of time. Thus, larger
© The authors 2014. Open Access under Creative Commons by
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82
Aquacult Environ Interact 6: 81–89, 2014
groups of hosts dilute the individual risk of being parasitized, a phenomenon termed the ‘dilution effect’
(Mooring & Hart 1992, Hart 1994). Côté & Poulin
(1995) found that the direction of effect of host density upon infection intensity was governed by parasite transmission mode. As such, the relationship
between host density and mean number of parasites
per host is positive for contagious or directly transmitted agents (transmitted through close proximity
between hosts or through infected faeces), and generally negative for mobile parasites which actively
seek their host.
Sea lice Lepeophtheirus salmonis and Caligus spp.
are external parasites responsible for multi-million
dollar losses in the salmon industry worldwide
(Costello 2009a), and for some of the major ecological
impacts associated with marine aquaculture (Krkošek et al. 2007, Costello 2009b, Torrissen et al. 2013).
Sea lice have a multiple-stage life cycle which includes free-swimming larvae and attached moult
stages. Larval nauplii and copepodid stages disperse
with water currents and seek potential hosts. When a
host is encountered, the lice attach and move into the
fixed chalimus stage (Bron et al. 1991). Lice become
mobile in the pre-adult and adult stages, and freely
move around their host (Morton et al. 2004, Hamre
et al. 2013) or transfer to other hosts when searching
for mates.
Copepodids, the mobile infective stage of L. salmonis, can alter their position in the water column in
response to environmental variables (Heuch et al.
1995, Tucker et al. 2000, Bricknell et al. 2006) and
actively seek their host (Heuch et al. 2007, Mordue
Luntz & Birkett 2009). As mobile parasites, an increase in host density could result in a lower infection
intensity. Few studies have tested this empirically
(Arneberg et al. 1998, Krasnov et al. 2002), particularly in aquatic systems (Sasal 2003), where disease
epidemiology is qualitatively different from terrestrial systems (McCallum et al. 2004). Infection success (Sevatdal 2001), prevalence (Krasnov et al. 2002,
Costello 2006) and degree of aggregation (Bagge et
al. 2005, Stanko et al. 2006, Krasnov et al. 2007) also
describe parasite infestation dynamics. Infection success is the proportion of parasites that successfully
infect a host out of the total number of parasites available per host during infection (i.e. infective dose) and
reflects parasite performance (Poulin & FitzGerald
1989). Prevalence is the proportion of infected hosts
and is expected to increase with host density due to
an increased probability for a parasite to encounter a
host. However, empirical studies in terrestrial systems (Morand & Guégan 2000, Šimková et al. 2002)
have indicated a positive correlation between prevalence and mean number of parasites per host, hence
prevalence would be higher at low host densities
for mobile parasites. Degree of aggregation refers to
the parasite distribution among individuals within a
group of hosts (Ives 1991) and is positively correlated
with prevalence (Stanko et al. 2006). Together, previous evidence from other systems suggests that host
density will influence most infection parameters in
the salmon−sea lice, host−parasite system.
Most artificial infection protocols using sea lice
only partially reflect how infection typically occurs in
the ocean for both wild and farmed fish. Artificial
infections add sea lice copepodids to tanks containing fish where water flow is turned off for several
hours (Grimnes & Jakobsen 1996, Dawson et al. 1997,
Bjørn & Finstad 1998, Hull et al. 1998, Bowers et al.
2000, Mustafa et al. 2000, Tucker et al. 2000), or place
anaesthetized fish in a concentrated suspension of
sea lice copepodids (Bricknell et al. 2006, Øverli et
al. 2014). The static water conditions during artificial
infections or immobile fish contrast with natural
infection settings for both farmed and wild salmon,
where water currents may change infection dynamics
(Revie et al. 2003). Here, we used swimming chambers with a fixed current speed that reflected the normal swimming speeds of salmon in farmed (Oppedal
et al. 2011a) and wild environments (Thorstad et al.
2012). This enabled more realistic swimming behaviours during infection to test how host density
influences infection intensity, infection success, prevalence and degree of aggregation of sea lice L.
salmonis on Atlantic salmon (Fig. 1).
We hypothesized that high host density would
result in a lower infection intensity, but similar infection success to low host density. High host density
would increase salmon−sea lice encounter rates, but
the higher infection dose at low host density would
also favour encounters, thus infection success would
be similar between density treatments. Further, we
hypothesized that the dilution effect of high host
density would decrease prevalence and degree of
aggregation. Finally, we predicted that prevalence
and infection intensity, and degree of aggregation and
infection intensity, would be positively correlated.
MATERIALS AND METHODS
Location and experimental setup
We conducted the experiment at the Matre research
station of the Institute of Marine Research, Norway.
Samsing et al.: High host densities dilute sea lice loads
83
Experimental fish
We used Atlantic salmon Salmo salar post-smolts
(Aquagen strain) which weighed on average 139.9 ±
10.1 g (mean ± SE) and were 23.2 ± 0.5 cm long (fork
length). Prior to infection, fish were acclimatized in
the raceways for 12 h. During sampling, individuals
were collected with a hand net, culled with a rapid
blow to the head and placed in individual plastic
bags to be analysed for the presence of sea lice.
Experimental design
Fig. 1. Predicted effects of differing host densities on
infection parameters. Infection intensity is the mean number of sea lice per fish. Prevalence is the proportion of
infected fish. Degree of aggregation refers to the parasite
distribution among host individuals, and was assessed
using Ives’ (1991) measure of intraspecific aggregation J,
defined as JA = VA / N 2 − 1 / N, where VA is the variance,
and N is the mean number of sea lice per fish. Infection
success is the percent of infection intensity out of the
total number of parasites available per host during infection (i.e. infective dose). We hypothesized that high host
density will decrease infection intensity, prevalence and
degree of aggregation due to the ‘dilution effect’, but
would have no effect on infection success, a parameter
that reflects parasite performance
For the trial, we used a set of tanks (3.0 m long ×
0.75 m deep, volume ∼5 m3; see the Appendix),
each holding a swimming chamber, or raceway.
Cylindrical raceways were 0.35 m in diameter and
2.25 m long, with an internal volume of 0.2 m3. A
laminar current speed of 16.2 ± 0.5 cm s−1 (mean ±
SE), equivalent to a swimming speed of 0.7 body
lengths (BL) s−1, was generated in the raceways by
a propeller (Minn Kota RT80/EM, Johnson Outdoors
Marine Electronics) with adjustable speed followed
by a honeycomb (5 mm opening, 102 mm thick, PC
5.0 G4, Plascore). Current speed was measured
with a Vectrino velocimeter (Nortek) and was set
at this level to reflect typical daytime swimming
speeds of salmon (Oppedal et al. 2011a, Thorstad
et al. 2012). Underwater video cameras (SeaVision
SV27) were mounted in the middle of each raceway, with the field of view covering the back of
the raceway. Water temperature during experiments was 13°C, salinity 34 ‰ and dissolved
oxygen concentration above 80% air saturation
throughout the infection period. Each raceway was
continuously illuminated (24:0 L:D regime) by a
36 W fluorescent tube suspended 35 cm above the
water surface.
To test the effect of host density on infection intensity (mean number of sea lice fish−1), infection success (% infection success = infection intensity × 100/
infective dose), prevalence (proportion of infected
fish) and degree of aggregation within the group of
fish, we randomly allocated 96 fish to each high density treatment replicate and 12 fish to each low density treatment replicate, and infected both with the
same estimated amount of infective copepodids.
Based on fish size, densities were 7.9 ± 1.0 kg m−3
and 68.5 ± 5.8 kg m−3 in the low and high density
treatments, respectively. These densities reflect realistic swimming densities experienced by farmed
salmon in sea-cages (Oppedal et al. 2011a,b). While
stocking density (total biomass of fish divided by
cage volume) is typically 5 to 25 kg m−3, swimming density is driven by fish actively choosing
specific depths based on environmental preferences
(Oppedal et al. 2011a). Swimming densities are typically 1.5 to 5 times the stocking densities, thus our 2
densities sit well within the lower and upper bounds
of known swimming densities (0 to 190 kg m−3;
Oppedal et al. 2011b).
We conducted the experiment in blocks to complete 4 replicates for each density treatment, and 10
fish were sampled 48 h after the start of the infection
to calculate the average data used for each replicate.
The degree of aggregation was assessed using
Ives’ (1991) measure of intraspecific aggregation J,
defined as:
JA = VA / N 2 − 1 / N
(1)
where VA is the variance, and N is the mean number
of sea lice fish−1 in each replicate. J is the increase in
the expected number of parasites that infect an average host above the number that would be expected
by chance if parasites were randomly distributed
among hosts. When J > 0, parasites are aggregated,
and when J = 0 they are randomly dispersed.
Aquacult Environ Interact 6: 81–89, 2014
84
Infection protocol
To source the sea lice copepodids that we used for
our experiments, we infected a separate group of 30
salmon with pre-adult II and adults of Lepeophtheirus salmonis collected from a farm in Solheim,
west coast of Norway. Fish were kept at 14°C until
female lice extruded egg strings, which were dissected and incubated in aquaria with a constant flow
of seawater at 15°C for 5 d until the development of
infective copepodids. Copepodid number was calculated indirectly by counting them in a 25 ml aliquot.
An estimate of 1000 copepodids was used to infect
both treatments. During infection, the sea lice suspension was added to the tanks, and the inflow of
water was stopped for 1 h to allow the larvae to recirculate, but the constant current inside the raceways
kept the fish swimming normally. After the first hour,
normal inflow was restored until sampling. Sampled
fish were analysed for the presence of sea lice, to
record total number and their location on the body
surface (including gills) and the fins.
Swimming behaviours
Behavioural differences between fish at different
densities could explain variations in infection parameters. Behavioural observations were made before
and during infection using underwater cameras, and
tanks were videotaped for a 10 min period each time.
The video recording, and thus the behavioural monitoring, was done before and immediately after introducing the infective sea lice copepodids into the tanks.
We recorded the total number of jumps, bursts, rolls
and muscle twitches on 5 randomly selected fish from
the third quarter of the raceway (i.e. readily visible
individuals). We observed each individual for 2 min
(Altmann 1974). Jumps were defined as fast acceleration in swimming speed that ended in a jump and contact with the surface; bursts were fast accelerations in
swimming speed performed against the current, without contact with the surface; rolls involved turning
90° on the horizontal or vertical plane, and swimming
in that position, without fast acceleration; and muscle
twitches were rapid spasmodic contractions of the
body, without relocation of more than 0.5 BL.
Statistical analysis
Differences in infection intensity, infection success,
prevalence and degree of aggregation between den-
sity treatments were compared with 1-way ANOVAs.
Infection intensity was log-transformed to improve
normality. Multiple transformations on the other
parameters did not markedly improve normality.
We calculated Pearson’s correlation coefficient to
explore the relationship between log mean sea lice
fish−1 and both prevalence and degree of aggregation using the data from each replicate in both treatments. We calculated the proportion of sea lice
attached to the body surface (including the gills)
and the fins, and compared these proportions using
1-way ANOVAs. These proportions were calculated
by dividing the total number of sea lice attached to a
body region by the total number of sea lice attached
to the sampled fish in each replicate. For the behavioural analysis, differences in counts of behaviours
(i.e. jumps, bursts, rolls and muscle twitches) between
treatments before infection, and differences between
treatments during infection were compared with
1-way ANOVAs. We compared the differences in
counts of behaviours before and during infection,
pooling over the effect of treatment, using paired
t-tests. Test assumptions of normality and homogeneity of variance were evaluated by assessing box plots
and plots of model residuals.
RESULTS
Sea lice infection intensity differed between the 2
density treatments; low density had 8.4 times more
lice per fish than high density (F1, 6 = 26, p = 0.001;
Fig. 2), which matches the 8 times more fish in the
high density. However, there were no differences in
Fig. 2. Mean ± SE (a) sea lice Lepeophtheirus salmonis per
salmon Salmo salar and (b) infection success (percent of infection intensity) after infection at low (12 fish; 7.9 ± 1.0 kg
m−3, mean ± SE; open bars) and high (96 fish; 68.5 ± 5.8 kg
m−3; grey bars) fish densities
Samsing et al.: High host densities dilute sea lice loads
85
have demonstrated that high host density reduces the mean number of sea
lice copepodids per host after infection.
Similar outcomes have been reported for
sticklebacks (Poulin & FitzGerald 1989),
lizards (Sorci et al. 1997) and reindeer
(Fauchald et al. 2007), whereby larger
group sizes decrease infection intensity.
Grouping or shoaling in fish is widely
recognized as an anti-predator mechanism
that exerts its effect through the dilution of the individual risk of being eaten
(Pitcher & Parrish 1993, Chivers et al.
Fig. 3. Correlations between prevalence and log mean sea lice Lepeoph1995). The term ‘dilution effect’ was
theirus salmonis per salmon Salmo salar, and degree of aggregation and
initially introduced by Hamilton (1971)
log mean sea lice fish−1
for predator−prey interactions, but the
term has also been used to define host−
infection success (F1, 6 = 0.04, p = 0.8), which was
parasite interactions (Schmidt & Ostfeld 2001), where
approximately 12% in both treatments, and no difthe increase in host density reduces the per capita
ferences in the proportion of sea lice copepodids
parasite load. The relationship between host density
attached to the body (F1, 6 = 0.98, p = 0.36) and the fins
and infection intensity appears to depend on the
(F1, 6 = 0.98, p = 0.36). Prevalence differed signifitransmission mode of the parasite (Côté & Poulin
cantly between density treatments (F1, 6 = 169, p <
1995). Sea lice copepodids are mobile crustaceans
0.001); all low density replicates had a prevalence of
(Pike & Wadsworth 1999, Bricknell et al. 2006), which
1 (i.e. all sampled fish were infected), whereas the
actively seek their host and display a range of hosthigh density treatment had an average prevalence of
finding behaviours when they detect host-related
0.68 ± 0.03 (mean ± SE). All J values were low (low
cues (Heuch et al. 2007, Mordue Luntz & Birkett
density: 0.10 ± 0.04; high density: 0.00 ± 0.14), indica2009). Mobile parasites searching for a host are analting that parasites were to some extent aggregated,
ogous to predators seeking their prey, and similarly
but the degree of aggregation did not vary between
the probability of an individual host being ‘attacked’
treatments (F1, 6 = 0.47, p = 0.52). Log mean sea lice
fish−1 and prevalence were strongly correlated (Pearson’s correlation: r2 = 0.98), with a weaker correlation
between log mean sea lice fish−1 and degree of
aggregation (r2 = 0.40; Fig. 3). Data from the low density treatment was normally distributed (AndersonDarling = 0.5; p = 0.2), whereas infection intensity
from fish in the high density level fitted a Poisson distribution (χ2 = 0.8; p = 0.7).
Before infection, the total count of behaviours was
higher in the low density treatment (3 times, F1, 6 =
14.7, p = 0.009). More bursts (3.2 times, F1, 6 = 21, p =
0.004) were observed during infection in the low
density treatment, but there were no significant differences between density levels in the other behaviours and in the total count of behaviours during
infection (F1, 6 = 21, p = 0.004; Fig. 4).
DISCUSSION
Here, we tested how host density influences sea
lice infection parameters on Atlantic salmon, and
Fig. 4. Mean ± SE count of behaviours (muscle twitches,
rolls, bursts, jumps) observed in salmon Salmo salar before
and during infection with sea lice Lepeophtheirus salmonis
copepodids at low (12 fish; 7.9 ± 1.0 kg m−3, mean ± SE) and
high (96 fish; 68.5 ± 5.8 kg m−3) fish densities
86
Aquacult Environ Interact 6: 81–89, 2014
decreases with group size. Therefore, the same ‘dilution effect’ that reduces predation in groups of prey
would reduce sea lice infection intensity in groups of
salmon at high densities.
In host−parasite interactions, the term ‘dilution
effect’ was originally applied to the prevalence of a
disease. Ostfeld & Keesing (2000) demonstrated that
increasing host density would reduce infection
prevalence, and therefore dilute the risk of vectorborne zoonoses. Here, we show that high fish density
diluted sea lice prevalence in a group of Atlantic
salmon post-smolts, an outcome that has been demonstrated for other host−parasite interactions in terrestrial systems (Krasnov et al. 2002, 2007). However,
overall infection success remained unchanged at
12% in both density treatments; hence parasite performance was not affected by fish density.
In most host−parasite interactions, the question
of whether infection parameters and their correlations are characteristic (= repeatable) of a particular
parasite species is commonly addressed (Poulin 2006,
Krasnov et al. 2007). We detected a strong positive
correlation between sea lice infection intensity and
prevalence, and a weaker positive correlation with
degree of aggregation, but no differences between
density treatments for this last parameter. The strong
positive correlation between prevalence and infection intensity has been demonstrated previously for
Lepeophtheirus salmonis on both wild (Heuch et
al. 2011) and farmed salmon (Baillie et al. 2009),
suggesting a characteristic pattern of infestations.
We detected a modest degree of aggregation, even
though sea lice infestations in both wild and farmed
salmon are generally aggregated (Costello 2006),
and this pattern is characteristic of this parasite species (Heuch et al. 2011). Therefore, our low degree
of aggregation could be an artefact of experimental
infections, which tend to be more homogeneous than
natural ones (Costello 2006). For mobile parasites,
prevalence is positively correlated to degree of aggregation, and therefore the latter is also positively
correlated to infection intensity (Stanko et al. (2006);
we also detected these relationships.
Sea lice distribution among hosts is related to prevalence, with a close fit to a negative binomial distribution at low prevalence levels (< 20%), a Poisson distribution at higher prevalence levels (70%),
and close to normal as prevalence approaches 100%
(Heuch et al. 2011), which aligns with our results.
Infection intensity can be estimated from prevalence
values and the negative binomial distribution that
characterizes most sea lice infestations (Baillie et al.
(2009). Consequently, as recording prevalence (pres-
ence or absence of a louse type) is far easier than
counting exact lice abundance, using a broader sampling strategy to estimate prevalence combined with
a more limited intensive sampling to estimate abundance, could provide a more accurate double metric
to estimate infection intensity (Baillie et al. 2009),
which is a mandatory process in salmon farms in
salmon-producing countries (Heuch et al. 2011).
Moreover, as sea lice infestations are usually aggregated, traditional sampling protocols used to calculate sea lice infection intensities in farms (e.g. sample
20 fish from 2 cages) may not give representative
values of the mean number of sea lice fish−1 in a
particular site or farm. Hence, calculating prevalence
to estimate mean number of sea lice fish−1 could allow
larger sample sizes from more sites or cages, improving the precision of sea lice surveillance protocols.
Behaviour is considered the first line of defence
against parasitism, and hosts normally display an
array of anti-parasite behaviours (Hart 1990, Barber
& Rushbrook 2008). The main purpose of measuring
behaviour in our study was to determine whether
differences in behavioural dynamism between density treatments would explain variations in infection
parameters. Fish in the high density treatment showed
fewer behavioural displays than fish at low densities
before infection; the more limited space available to
individuals in the higher density appears to have
reduced the expression of specific behaviours. However, in both treatments, the frequency of observed
behaviours increased during infection, with only
bursts being more prevalent in the low density treatment. This increase in behavioural dynamism could
be a reaction to the presence of sea lice copepodids,
or an evolved behaviour to evade them. Changes in
behaviour of fish exposed to ectoparasitic copepods
have been reported previously for Atlantic salmon exposed to L. salmonis (Webster et al. 2007) and brook
trout exposed to Salmincola edwarsii (Poulin et al.
1991). However, determining whether these behaviours effectively reduce infestations requires further
study. As major differences in behaviour did not exist
between density treatments during infection, we
assume that fish in both treatments were able to
broadly display their behaviours. Both treatments
had similar infection success, thus we can assume
that neither density nor behaviour are affecting
parasite performance. Further, behaviour could be a
mechanism to deflect the body location of attached
ectoparasites on a host (e.g. Sears et al. 2013). However, the proportion of copepodids attached to the
body or the fins did not differ between treatments,
further supporting the assumption that fish in high
Samsing et al.: High host densities dilute sea lice loads
and low densities could manifest their behaviours
without restrictions.
Stocking density of farmed fish is a central topic
in aquaculture, and various studies have examined
its effect on fish welfare. For example, Arctic charr
Salvelinus alpinus show fewer agonistic behaviours
and higher growth rates at high stocking densities
(Brown et al. 1992). Similarly, Adams et al. (2007)
demonstrated that aggressive interactions were negatively related to stocking density in Atlantic salmon,
and their overall welfare score was best at an intermediate density of 25 kg m−3, indicating that both low
and high densities may affect welfare. If densities
become too high, oxygen depletion becomes problematic (Oppedal et al. 2011b), and environmental hypoxia may reduce salmon welfare. Moreover,
studies generally use absolute stocking densities (i.e.
based on total biomass), which seldom reflect the
actual fish density inside the cage as fish choose to
congregate at specific depths depending on environmental conditions (Oppedal et al. 2011a). For farmed
Atlantic salmon, swimming densities typically exceed stocking densities by 1.5 to 5 times, and up to
17 times in specific environments (Oppedal et al.
2011a,b), and this has to be considered when planning strategic procedures to improve welfare at the
farm level.
Combined with an understanding of sea lice loads
in the water column and environmental data, our
results could be useful in opening new strategies to
improve farmed salmon welfare. Salmon swimming
behaviour and their vertical position in the water
column is strongly influenced by light levels (Tang et
al. 2011), and can be manipulated by using artificial
light (Juell et al. 2003, Oppedal et al. 2007, Frenzl et
al. 2014). Sea lice copepodids are phototactic and
gather near surface waters during the day, while also
actively avoiding low salinities (Heuch et al. 1995,
Costello 2006). Consequently, a combination of procedures to keep salmon away from parasite-risky
areas in surface layers (F. Oppedal et al. unpubl.), and
increase the swimming density of fish deeper in the
cage to dilute the individual risk of being parasitized,
could produce the best overall effect for salmon welfare in farming environments. Further, the observed
behavioural change could be used as an operational
welfare indicator giving the farmer an early warning.
Acknowledgements. We thank Marsela Alvanopoulou, Ole
Fredrik Skulstad and Enrique García for technical assistance during the experiments. Funding was provided by the
Norwegian Research Council through the project Exposed
Salmon Farming in High Current and Waves (207116/E40)
87
and Behaviour and Coping of Individual Salmon in Farm
Environments with Fluctuating Oxygen and Hydrodynamics
(206968). The work was conducted in accordance with the
laws and regulations of the Norwegian Regulation on Animal Experimentation 1996 under permit number 6221.
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Appendix. Schematic of the tank (3 m × 0.75 m depth; volume ~5 m3) holding the experimental raceway (2 m × 0.1 m2 transsectional area; volume ~0.2 m3) with underwater camera. Raceways were fitted with a propeller with adjustable speed
followed by a honeycomb to generate the laminar flows
Editorial responsibility: Bengt Finstad,
Trondheim, Norway
Submitted: August 1, 2014; Accepted: October 20, 2014
Proofs received from author(s): November 22, 2014